Applied Scientist Ii, Last Mile Address Intelligence

DESCRIPTIONCustomer addresses Geospatial information and Road-network play a crucial role in Amazon Logistics Delivery Planning systems We own exciting science problems in the areas of Address Normalization Geocode learning Maps learning Time estimations including route-time delivery-time transit-time predictions which are key inputs in delivery planning As part of the Geospatial science team within Last Mile you will partner closely with other scientists and engineers in a collegial environment to develop enterprise ML solutions with a clear path to business impact The setting also gives you an opportunity to think about a complex large-scale problem for multiple years and building increasingly sophisticated solutions year over year In the process there will be opportunity to innovate explore SOTA and publish the research in internal and external ML conferences Successful candidates will have deep knowledge of competing machine learning methods for large scale predictive modelling natural language processing semi-supervised graph based learning We also look for the experience to graduate prototype models to production and the communication skills to explain complex technical approaches to the stakeholders of varied technical expertise Here is a glimpse of the problem spaces and technologies that we deal with on a regular basis 1 De-duping and organizing addresses into hierarchy while handling noisy inconsistent localized and multi-lingual user inputs We do this at the scale of millions of customers for established US EU as well as emerging geographies IN MX We make use of technologies like LLMs Weak supervision Graph-based clustering Entity matching We also use additional modalities such as building outlines in maps street view images and 3P datasets gazetteers 2 Build a generic ML framework which leverages relationship between places to improve delivery experience by learning precise delivery locations and propagating attributes such as business hours and safe places This requires us to combine a variety of inputs maps delivery locations defects effectively work in a multi-objective setting and exploit semantic as well as structural properties of places 3 Build LLMs and Foundational models that are specialized for Geospatial domain to perform multitasking address parsing validation normalization completion etc We also use in-context and retrieval augmented learning to utilize real-world contextual information to ground the model predictions 4 Work done in sister teams Developing systems to consume inputs from areal imagery and optimize our maps to enable efficient delivery planning Also building models to estimate travel time turn costs optimal route and defect propensities For these problems we make use of multiple CV Optimization TSP Counterfactual analysis and other supervised learning techniques that can operate at scale Key job responsibilities As an Applied Scientist II your responsibility will be to deliver on a well defined but complex business problem explore SOTA technologies including GenAI and customize the large models as suitable for the application Your job will be to work on a end-to-end business problem from design to experimentation and implementation There is also an opportunity to work on open ended ML directions within the space and publish the work in prestigious ML conferences About the team Last Mile Address Intelligence LMAI team is led by Saurabh Sohoney and is spread across HYD13 and BLR26 locations LMAI team owns WW charter for address and location learning solutions which are crucial for efficient Last Mile delivery planning The HM for this role- Sayan Putatunda reports to Saurabh and is based out of Bangalore LMAI is a part of Geospatial science team led by Amber Roy Chowdhury who also owns problems in the space of maps learning and travel time estimations His rest of the team and senior leadership of Last Mile org works out of Bellevue office BASIC QUALIFICATIONS- 3 years of building models for business application experience - Experience in patents or publications at top-tier peer-reviewed conferences or journals - Experience programming in Java C Python or related language - Experience in any of the following areas algorithms and data structures parsing numerical optimization data mining parallel and distributed computing high-performance computingPREFERRED QUALIFICATIONS- Experience using Unix Linux - Experience in professional software development - Master s degree